Coherent joint transmission (CJT) is a promising technology to improve the spectral efficiency of multi-cell communication systems. In this paper, we investigate the transmit power minimization problem in multi-cell CJT networks. To realize the decentralized coordination that avoids the costly exchange of instantaneous channel state information, we leverage the deterministic equivalents for the approximation of inter-cell interference to decouple the overall precoding problem into independent sub-problems, which can be solved locally at each base station. In addition, to compensate the performance loss caused by the channel aging, we further propose a robust precoding scheme and develop an efficient solver based on the alternating direction method of multiplier. Extensive numerical results show that both the decentralized coordination and robust precoding design provide our proposed method with performance improvement.

In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission. To avoid costly information exchange among the cooperating base stations in a centralized precoding scheme, we propose a decentralized one by considering the power minimization problem. By approximating the inter-cell interference using the deterministic equivalents, this problem is decoupled to sub-problems which are solved in a decentralized manner at different base stations. Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, where only 2 ∼ 7% more transmit power is needed compared with the optimal centralized precoder.

There is an increasing demand of massive machine-type communication (mMTC) to provide scalable access for a large number of devices, which has prompted extensive investigation on grant-free massive random access (RA) in 5G and beyond wireless networks. Although many efficient signal processing algorithms have been developed, the limited radio resource for pilot transmission in grant-free massive RA systems makes accurate user activity detection and channel estimation challenging, which thereby compromises the communication reliability. In this paper, we adopt retransmission as a means to improve the quality of service (QoS) for grant-free massive RA. Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm. Then, we analyze the performance of the proposed receiver, including the user activity detection, channel estimation, and data error, by resorting to the state evolution of the correlated AMP algorithm and the random matrix theory (RMT). Our analysis admits a tight closed-form approximation for frame error rate (FER) evaluation. Simulation results corroborate our theoretical analysis and demonstrate the effectiveness of the proposed receiver for grant-free massive RA with retransmission, compared with a conventional design that disregards the critical user activity correlation.

To realize underwater accurate vision-based operations, this article proposes a hybrid visual servoing control scheme for underwater vehicle manipulator systems (UVMSs) toward a moving target. First, the position-based visual servoing (PBVS) scheme is purposed based on the binocular camera. Second, to reduce the influence of hand-eye system uncertainties, an uncalibrated visual servoing (UVS) control scheme based on the monocular camera is presented. The partitioned Broyden’s method is utilized to estimate the Jacobian matrix, and a modified adaptive Broyden’s class method is proposed to obtain the approximation of the residual matrix. Afterward, in order to realize long range accurate operations, a novel hybrid visual servoing scheme with Jacobian matrix fusion algorithm is presented not only to combine the PBVS scheme and the UVS scheme but also to avoid the trial movements’ influence. Simulations and an experiment are conducted to testify the robustness and effectiveness of the presented scheme.

Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice. Therefore, this paper considers asynchronous grant-free massive RA systems and develops novel algorithms for joint user activity detection, synchronization delay detection, and channel estimation. In particular, the framework of orthogonal approximate message passing (OAMP) is first utilized to deal with the non-independent and identically distributed (i.i.d.) pilot matrix in asynchronous grant-free massive RA systems, and an OAMP-based algorithm capable of leveraging the common sparsity among the received pilot signals from multiple base station antennas is developed. To reduce the computational complexity, a memory AMP (MAMP)-based algorithm is further proposed that eliminates the matrix inversions in the OAMP-based algorithm. Simulation results demonstrate the effectiveness of the two proposed algorithms over the baseline methods. Besides, the MAMP-based algorithm reduces 37% of the computations while maintaining comparable detection/estimation accuracy, compared with the OAMP-based algorithm.

In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. Recently, grant-free random access has emerged as a promising mechanism for this challenging scenario, but its potential has not been fully unleashed. In particular, the available auxiliary information has not been fully exploited, including the common sparsity pattern in the received pilot and data signal, as well as the channel decoding information. This article develops advanced receivers in a holistic manner to improve the massive access performance by jointly designing activity detection, channel estimation, and data decoding. To tackle the algorithmic and computational challenges, a turbo structure is adopted at the joint receiver. For performance enhancement, all the received symbols are utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information (SI)-aided receiver is also proposed, where the channel decoder provides SI to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, supporting twice as many active users compared with a separate design that disregards the common sparsity. In addition, the SI-aided receiver notably outperforms the conventional methods with a relatively low complexity.

Grant-free massive random access (RA) is a promising protocol to support the massive machine-type communications (mMTC) scenario in 5G and beyond networks. In this paper, we focus on the error rate analysis in grant-free massive RA, which is critical for practical deployment but has not been well studied. We consider a two-phase frame structure, with a pilot transmission phase for activity detection and channel estimation, followed by a data transmission phase with coded data symbols. Considering the characteristics of short-packet transmission, we analyze the block error rate (BLER) in the finite blocklength regime to characterize the data transmission performance. The analysis involves characterizing the activity detection and channel estimation errors as well as applying the random matrix theory (RMT) to analyze the distribution of the post-processing signal-to-noise ratio (SNR). As a case study, the derived BLER expression is further simplified to optimize the pilot length. Simulation results verify our analysis and demonstrate its effectiveness in pilot length optimization.

Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. Particularly, the two key tasks in massive access systems, namely, user activity detection and data detection, were handled separately in most existing studies, which ignored the common sparsity pattern in the received pilot and data signal. Moreover, error detection and correction in the payload data provide additional mechanisms for performance improvement. In this paper, we propose a data-assisted activity detection framework, which aims at supporting more active users by reducing the activity detection error, consisting of false alarm and missed detection errors. Specifically, after an initial activity detection step based on the pilot symbols, the false alarm users are filtered by applying energy detection for the data symbols; once data symbols of some active users have been successfully decoded, their effect in activity detection will be resolved via successive pilot interference cancellation, which reduces the missed detection error. Simulation results show that the proposed algorithm effectively increases the activity detection accuracy, and it is able to support ∼20% more active users compared to a conventional method in some sample scenarios.